rm(list=ls())
gc()
##############################################
##############################################
##### 3 - ALTERNATIVE EXPLANATIONS ###########               
#### MOVER, URBAN/RURAL, SOUTH/NONSOUTH ######
########### MODERATING EFFECTS ###############    
##############################################
##############################################
# Here, we estimate the effect of a Black next door neighbor
# on subsets defined by the variables mapping onto alternative explanations:
# non-movers, urban residents, and non-South residents. 
# This script outputs Figure 3 in the main text.


## LOAD PACKAGES
require(sandwich)
require(lmtest)
require(lfe)
require(gtools)
require(tidyverse)
require(stargazer)
require(reshape)
require(data.table)

## LOAD DATA
load("02-data/befm-main-analysis.Rdata")
df = as.data.table(df)

##############################
####### MAKE VARIABLES #######
####### FOR ANALYSIS   #######
##############################

df$white = ifelse(df$race == 100, 1, 0) # WHITE INDICATOR

#### FIXED EFFECT VARIABLES

# county
df$county_state = paste0(df$statefip, "_", df$county)  

# enumdist
df$cnty_enumdist = paste0(df$county_state, "_", df$supdist,"_",df$enumdist)
df$cnty_enumdist = ifelse(grepl('NA', df$cnty_enumdist), NA, df$cnty_enumdist)

# 10 page
df$page10 = round(df$pageno, digits = -1)
df$reel_page10 = paste0(df$reel, "_", df$page10)

# 5 page
mround <- function(x,base){ 
  base*round(x/base) }
df$page5 = mround(df$pageno, 5)
df$reel_page5 = paste0(df$reel, "_", df$page5)



# HOMEOWNERSHIP
df$homeowner = ifelse(df$ownershp==10,1,ifelse(df$ownershp==20,0,NA))

### INDICATOR FOR NEIGHBOR WITH HIGH SCHOOL DEGREE

df$hs_dist1 = ifelse((df$hs_opposite1_dist_1 == 1 & df$hs == 0) | (df$hs_opposite1_dist_1 == 0 & df$hs == 1),1,0)
df$hs_dist2 = ifelse((df$hs_opposite1_dist_2 == 1 & df$hs == 0) | (df$hs_opposite1_dist_2 == 0 & df$hs == 1),1,0)
df$hs_dist3 = ifelse((df$hs_opposite1_dist_3 == 1 & df$hs == 0) | (df$hs_opposite1_dist_3 == 0 & df$hs == 1),1,0)
df$hs_dist4 = ifelse((df$hs_opposite1_dist_4 == 1 & df$hs == 0) | (df$hs_opposite1_dist_4 == 0 & df$hs == 1),1,0)
df$hs_dist5 = ifelse((df$hs_opposite1_dist_5 == 1 & df$hs == 0) | (df$hs_opposite1_dist_5 == 0 & df$hs == 1),1,0)
df$hs_dist6 = ifelse((df$hs_opposite1_dist_6 == 1 & df$hs == 0) | (df$hs_opposite1_dist_6 == 0 & df$hs == 1),1,0)
df$hs_dist7 = ifelse((df$hs_opposite1_dist_7 == 1 & df$hs == 0) | (df$hs_opposite1_dist_7 == 0 & df$hs == 1),1,0)
df$hs_dist8 = ifelse((df$hs_opposite1_dist_8 == 1 & df$hs == 0) | (df$hs_opposite1_dist_8 == 0 & df$hs == 1),1,0)
df$hs_dist9 = ifelse((df$hs_opposite1_dist_9 == 1 & df$hs == 0) | (df$hs_opposite1_dist_9 == 0 & df$hs == 1),1,0)
df$hs_dist10 = ifelse((df$hs_opposite1_dist_10 == 1 & df$hs == 0) | (df$hs_opposite1_dist_10 == 0 & df$hs == 1),1,0)


df$employed = ifelse(df$empstat %in% 10:15,1,ifelse(df$empstat%in%20:29,0,NA))

## THERE IS A CLUSTER OF LINKED VOTERS WITH VOTERFILE AGE OVER 100 THAT ARE LIKELY MISRECORDED.
## IN THE 2005/2009 SAMPLE WE RESTRICT SAMPLE TO VOTERS WITH 1940 AGES SUCH THAT THEY ARE NOT OVER 100 IN CONTEMPORARY VOTERFILES
df = df[(df$state.vfile == 'CA05' & age <=35) |(df$state.vfile == 'NC09' & age <=31),]

##############################
##### 2005/2009 Analysis #####
##############################


#####################################
#### SET COVARIATES FOR FORMULAS ####
#####################################

# for models with covariates
covars = c( 
  "age", "hs","incwage", "mover","hoh_age", "homeowner",
  "famsize", 'employed', 'hrswork1', 'wkswork1',
  paste0("opposite1_dist_", 1:10), paste0("hs_dist", 1:10))

# for models with no covariates
#nocovars = paste0("opposite1_dist_", 1:10)


####################################
#### First Category: Non-Movers ####
####################################

#########################
#### ESTIMATE MODELS ####
#########################


## NO FE
fml_dem = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|0|0|county_state",sep=""))
mod_dem1 = felm(formula=fml_dem, data = df[white==1 & mover == 0,] )
sum_dem1 = summary(mod_dem1, robust =T)

## STATE FE
fml_dem_st = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|statefip|0|county_state",sep=""))
mod_dem_st = felm(formula=fml_dem_st, data = df[white==1 & mover == 0,] )
sum_dem_st=summary(mod_dem_st, robust =T)

## COUNTY FE
fml_dem_c = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|county_state|0|county_state",sep=""))
mod_dem_c = felm(formula=fml_dem_c, data = df[white==1 & mover == 0,] )
sum_dem_c=summary(mod_dem_c, robust =T)


## ENUMDIST FE
fml_dem_ed = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|cnty_enumdist|0|county_state",sep=""))
mod_dem_ed= felm(formula=fml_dem_ed, data = df[white==1 & mover == 0,] )
sum_dem_ed=summary(mod_dem_ed, robust =T)



## REEL FE
fml_dem_r = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel|0|county_state",sep=""))
mod_dem_r = felm(formula=fml_dem_r, data = df[white==1 & mover == 0,] )
sum_dem_r=summary(mod_dem_r, robust =T)



## REEL PAGE 5 FE
fml_dem_rp5 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page5|0|county_state",sep=""))
mod_dem_rp5= felm(formula=fml_dem_rp5, data = df[white==1 & mover == 0,] )
sum_dem_rp5=summary(mod_dem_rp5, robust =T)


## REEL PAGE 10 FE
fml_dem_rp10 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page10|0|county_state",sep=""))
mod_dem_rp10 = felm(formula=fml_dem_rp10, data = df[white==1 & mover == 0,] )
sum_dem_rp10 = summary(mod_dem_rp10, robust =T)



## MAKE PLOT DATA
plot_data = as.data.frame(matrix(c(sum_dem1$coefficients['opposite1_dist_1',c(1,2,4)], "controls",  "none"), ncol =5, nrow=1), stringsAsFactors = F)
colnames(plot_data) = c("coef", "se", "p-value", "covars", 
                        "fe")

plot_data = rbind(plot_data, c(sum_dem_st$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "state"))
plot_data = rbind(plot_data, c(sum_dem_c$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "county"))
plot_data = rbind(plot_data, c(sum_dem_r$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "reel"))
plot_data = rbind(plot_data, c(sum_dem_rp5$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-5"))
plot_data = rbind(plot_data, c(sum_dem_rp10$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-10"))
plot_data = rbind(plot_data, c(sum_dem_ed$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "enumdist"))


plot_data$coef=as.numeric(plot_data$coef)
plot_data$se=as.numeric(plot_data$se)
plot_data$`p-value`=as.numeric(plot_data$`p-value`)
plot_data$covars = factor(plot_data$covars, levels = c("no controls", "controls"))
plot_data$fe=factor(plot_data$fe, levels = c("none", "state", "county", "enumdist", "reel","page-10",
                                             "page-5"))




plot_data_mover = plot_data



rm(plot_data)
rm(list=ls()[ls()!='df'& !grepl('covars',ls())& !grepl('plot_data', ls())])
gc()


#####

########################################################
#### Second Category: South vs Non South ####
########################################################

#########################
#### ESTIMATE MODELS ####
#########################


## NO FE
fml_dem = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|0|0|county_state",sep=""))
mod_dem1 = felm(formula=fml_dem, data = df[white==1 & south == 0,] )
sum_dem1 = summary(mod_dem1, robust =T)




## STATE FE
fml_dem_st = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|statefip|0|county_state",sep=""))
mod_dem_st = felm(formula=fml_dem_st, data = df[white==1 & south == 0,] )
sum_dem_st=summary(mod_dem_st, robust =T)


## COUNTY FE
fml_dem_c = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|county_state|0|county_state",sep=""))
mod_dem_c = felm(formula=fml_dem_c, data = df[white==1 & south == 0,] )
sum_dem_c=summary(mod_dem_c, robust =T)


## ENUMDIST FE
fml_dem_ed = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|cnty_enumdist|0|county_state",sep=""))
mod_dem_ed= felm(formula=fml_dem_ed, data = df[white==1 & south == 0,] )
sum_dem_ed=summary(mod_dem_ed, robust =T)



## REEL FE
fml_dem_r = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel|0|county_state",sep=""))
mod_dem_r = felm(formula=fml_dem_r, data = df[white==1 & south == 0,] )
sum_dem_r=summary(mod_dem_r, robust =T)


## REEL PAGE 5 FE
fml_dem_rp5 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page5|0|county_state",sep=""))
mod_dem_rp5= felm(formula=fml_dem_rp5, data = df[white==1 & south == 0,] )
sum_dem_rp5=summary(mod_dem_rp5, robust =T)

## REEL PAGE 10 FE
fml_dem_rp10 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page10|0|county_state",sep=""))
mod_dem_rp10 = felm(formula=fml_dem_rp10, data = df[white==1 & south == 0,] )
sum_dem_rp10 = summary(mod_dem_rp10, robust =T)


## MAKE PLOT DATA
plot_data = as.data.frame(matrix(c(sum_dem1$coefficients['opposite1_dist_1',c(1,2,4)], "controls",  "none"), ncol =5, nrow=1), stringsAsFactors = F)
colnames(plot_data) = c("coef", "se", "p-value", "covars", 
                        "fe")

plot_data = rbind(plot_data, c(sum_dem_st$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "state"))
plot_data = rbind(plot_data, c(sum_dem_c$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "county"))
plot_data = rbind(plot_data, c(sum_dem_r$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "reel"))
plot_data = rbind(plot_data, c(sum_dem_rp5$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-5"))
plot_data = rbind(plot_data, c(sum_dem_rp10$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-10"))
plot_data = rbind(plot_data, c(sum_dem_ed$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "enumdist"))



plot_data$coef=as.numeric(plot_data$coef)
plot_data$se=as.numeric(plot_data$se)
plot_data$`p-value`=as.numeric(plot_data$`p-value`)
plot_data$covars = factor(plot_data$covars, levels = c("no controls", "controls"))
plot_data$fe=factor(plot_data$fe, levels = c("none", "state", "county", "enumdist", "reel","page-10",
                                             "page-5"))




plot_data_south = plot_data



rm(plot_data)
rm(list=ls()[ls()!='df'& !grepl('covars',ls())& !grepl('plot_data', ls())])
gc()


#####

########################################
#### Third Category: Urban/Rural ####
########################################

#########################
#### ESTIMATE MODELS ####
#########################


## NO FE
fml_dem = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|0|0|county_state",sep=""))
mod_dem1 = felm(formula=fml_dem, data = df[white==1 & urban == 1,] )
sum_dem1 = summary(mod_dem1, robust =T)

## STATE FE
fml_dem_st = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|statefip|0|county_state",sep=""))
mod_dem_st = felm(formula=fml_dem_st, data = df[white==1 & urban == 1,] )
sum_dem_st=summary(mod_dem_st, robust =T)


## COUNTY FE
fml_dem_c = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|county_state|0|county_state",sep=""))
mod_dem_c = felm(formula=fml_dem_c, data = df[white==1 & urban == 1 ,] )
sum_dem_c=summary(mod_dem_c, robust =T)


## ENUMDIST FE
fml_dem_ed = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|cnty_enumdist|0|county_state",sep=""))
mod_dem_ed= felm(formula=fml_dem_ed, data = df[white==1 & urban == 1,] )
sum_dem_ed=summary(mod_dem_ed, robust =T)



## REEL FE
fml_dem_r = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel|0|county_state",sep=""))
mod_dem_r = felm(formula=fml_dem_r, data = df[white==1 & urban == 1,] )
sum_dem_r=summary(mod_dem_r, robust =T)


## REEL PAGE 5 FE
fml_dem_rp5 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page5|0|county_state",sep=""))
mod_dem_rp5= felm(formula=fml_dem_rp5, data = df[white==1  & urban == 1,] )
sum_dem_rp5=summary(mod_dem_rp5, robust =T)

## REEL PAGE 10 FE
fml_dem_rp10 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page10|0|county_state",sep=""))
mod_dem_rp10 = felm(formula=fml_dem_rp10, data = df[white==1  & urban == 1,] )
sum_dem_rp10 = summary(mod_dem_rp10, robust =T)

## MAKE PLOT DATA
plot_data = as.data.frame(matrix(c(sum_dem1$coefficients['opposite1_dist_1',c(1,2,4)], "controls",  "none"), ncol =5, nrow=1), stringsAsFactors = F)
colnames(plot_data) = c("coef", "se", "p-value", "covars", 
                        "fe")

plot_data = rbind(plot_data, c(sum_dem_st$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "state"))
plot_data = rbind(plot_data, c(sum_dem_c$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "county"))
plot_data = rbind(plot_data, c(sum_dem_r$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "reel"))
plot_data = rbind(plot_data, c(sum_dem_rp5$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-5"))
plot_data = rbind(plot_data, c(sum_dem_rp10$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-10"))
plot_data = rbind(plot_data, c(sum_dem_ed$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "enumdist"))



plot_data$coef=as.numeric(plot_data$coef)
plot_data$se=as.numeric(plot_data$se)
plot_data$`p-value`=as.numeric(plot_data$`p-value`)
plot_data$covars = factor(plot_data$covars, levels = c("no controls", "controls"))
plot_data$fe=factor(plot_data$fe, levels = c("none", "state", "county", "enumdist", "reel","page-10",
                                             "page-5"))




plot_data_urban = plot_data




rm(plot_data)
rm(list=ls()[ls()!='df'& !grepl('covars',ls())& !grepl('plot_data', ls())])
gc()




#####





plot_data_0509 = plot_data_mover %>%
  as_tibble %>%
  mutate(analysis = 'No Move in Last 5 Years',
         year  = '2005/2009 Sample') %>%
  bind_rows(plot_data_south %>%
              as_tibble %>%
              mutate(analysis = 'Non-South',
                     year  = '2005/2009 Sample'))%>%
  bind_rows(plot_data_urban %>%
              as_tibble %>%
              mutate(analysis = 'Urban',
                     year  = '2005/2009 Sample'))
rm(plot_data_mover, plot_data_south, plot_data_urban, df)
gc()
############################################################

####2017 Analysis

## LOAD DATA
load("02-data/seg_analysis_2017.Rdata")
df=as.data.table(df)
##############################
####### MAKE VARIABLES #######
####### FOR ANALYSIS   #######
##############################

df$white = ifelse(df$race == 100, 1, 0) # WHITE INDICATOR

# CREATE SOUTH VARIABLE
#df$south = ifelse(df$statefip %in% c(1, 5, 12, 13, 21, 22, 28, 37, 45, 48, 51, 10, 40, 54, 47, 24), 1, 0)


#### FIXED EFFECT VARIABLES

# county
df$county_state = paste0(df$statefip, "_", df$county)  

# enumdist
df$cnty_enumdist = paste0(df$county_state, "_", df$supdist,"_",df$enumdist)
df$cnty_enumdist = ifelse(grepl('NA', df$cnty_enumdist), NA, df$cnty_enumdist)

# 10 page
df$page10 = round(df$pageno, digits = -1)
df$reel_page10 = paste0(df$reel, "_", df$page10)

# 5 page
mround <- function(x,base){ 
  base*round(x/base) }
df$page5 = mround(df$pageno, 5)
df$reel_page5 = paste0(df$reel, "_", df$page5)

# HOMEOWNERSHIP
df$homeowner = ifelse(df$ownershp==10,1,ifelse(df$ownershp==20,0,NA))

### INDICATOR FOR NEIGHBOR WITH HIGH SCHOOL DEGREE

df$hs_dist1 = ifelse((df$hs_opposite1_dist_1 == 1 & df$hs == 0) | (df$hs_opposite1_dist_1 == 0 & df$hs == 1),1,0)
df$hs_dist2 = ifelse((df$hs_opposite1_dist_2 == 1 & df$hs == 0) | (df$hs_opposite1_dist_2 == 0 & df$hs == 1),1,0)
df$hs_dist3 = ifelse((df$hs_opposite1_dist_3 == 1 & df$hs == 0) | (df$hs_opposite1_dist_3 == 0 & df$hs == 1),1,0)
df$hs_dist4 = ifelse((df$hs_opposite1_dist_4 == 1 & df$hs == 0) | (df$hs_opposite1_dist_4 == 0 & df$hs == 1),1,0)
df$hs_dist5 = ifelse((df$hs_opposite1_dist_5 == 1 & df$hs == 0) | (df$hs_opposite1_dist_5 == 0 & df$hs == 1),1,0)
df$hs_dist6 = ifelse((df$hs_opposite1_dist_6 == 1 & df$hs == 0) | (df$hs_opposite1_dist_6 == 0 & df$hs == 1),1,0)
df$hs_dist7 = ifelse((df$hs_opposite1_dist_7 == 1 & df$hs == 0) | (df$hs_opposite1_dist_7 == 0 & df$hs == 1),1,0)
df$hs_dist8 = ifelse((df$hs_opposite1_dist_8 == 1 & df$hs == 0) | (df$hs_opposite1_dist_8 == 0 & df$hs == 1),1,0)
df$hs_dist9 = ifelse((df$hs_opposite1_dist_9 == 1 & df$hs == 0) | (df$hs_opposite1_dist_9 == 0 & df$hs == 1),1,0)
df$hs_dist10 = ifelse((df$hs_opposite1_dist_10 == 1 & df$hs == 0) | (df$hs_opposite1_dist_10 == 0 & df$hs == 1),1,0)


df$employed = ifelse(df$empstat %in% 10:15,1,ifelse(df$empstat%in%20:29,0,NA))



#####################################
#### SET COVARIATES FOR FORMULAS ####
#####################################


# for models with covariates
covars = c( 
  "age", "hs","incwage", "mover","hoh_age", "homeowner",
  "famsize", 'employed',  'hrswork1', 'wkswork1',
  paste0("opposite1_dist_", 1:10), paste0("hs_dist", 1:10))


# for models with no covariates
#nocovars = paste0("opposite1_dist_", 1:10)


####################################
#### First Category: Non-Movers ####
####################################

#########################
#### ESTIMATE MODELS ####
#########################


## NO FE
fml_dem = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|0|0|county_state",sep=""))
mod_dem1 = felm(formula=fml_dem, data = df[white==1 & mover == 0,] )
sum_dem1 = summary(mod_dem1, robust =T)


## STATE FE
fml_dem_st = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|statefip|0|county_state",sep=""))
mod_dem_st = felm(formula=fml_dem_st, data = df[white==1 & mover == 0,] )
sum_dem_st=summary(mod_dem_st, robust =T)

## COUNTY FE
fml_dem_c = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|county_state|0|county_state",sep=""))
mod_dem_c = felm(formula=fml_dem_c, data = df[white==1 & mover == 0,] )
sum_dem_c=summary(mod_dem_c, robust =T)

## ENUMDIST FE
fml_dem_ed = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|cnty_enumdist|0|county_state",sep=""))
mod_dem_ed= felm(formula=fml_dem_ed, data = df[white==1 & mover == 0,] )
sum_dem_ed=summary(mod_dem_ed, robust =T)


## REEL FE
fml_dem_r = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel|0|county_state",sep=""))
mod_dem_r = felm(formula=fml_dem_r, data = df[white==1 & mover == 0,] )
sum_dem_r=summary(mod_dem_r, robust =T)


## REEL PAGE 5 FE
fml_dem_rp5 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page5|0|county_state",sep=""))
mod_dem_rp5= felm(formula=fml_dem_rp5, data = df[white==1 & mover == 0,] )
sum_dem_rp5=summary(mod_dem_rp5, robust =T)

## REEL PAGE 10 FE
fml_dem_rp10 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page10|0|county_state",sep=""))
mod_dem_rp10 = felm(formula=fml_dem_rp10, data = df[white==1 & mover == 0,] )
sum_dem_rp10 = summary(mod_dem_rp10, robust =T)


## MAKE PLOT DATA
plot_data = as.data.frame(matrix(c(sum_dem1$coefficients['opposite1_dist_1',c(1,2,4)], "controls",  "none"), ncol =5, nrow=1), stringsAsFactors = F)
colnames(plot_data) = c("coef", "se", "p-value", "covars", 
                        "fe")

plot_data = rbind(plot_data, c(sum_dem_st$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "state"))
plot_data = rbind(plot_data, c(sum_dem_c$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "county"))
plot_data = rbind(plot_data, c(sum_dem_r$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "reel"))
plot_data = rbind(plot_data, c(sum_dem_rp5$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-5"))
plot_data = rbind(plot_data, c(sum_dem_rp10$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-10"))
plot_data = rbind(plot_data, c(sum_dem_ed$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "enumdist"))


plot_data$coef=as.numeric(plot_data$coef)
plot_data$se=as.numeric(plot_data$se)
plot_data$`p-value`=as.numeric(plot_data$`p-value`)
plot_data$covars = factor(plot_data$covars, levels = c("no controls", "controls"))
plot_data$fe=factor(plot_data$fe, levels = c("none", "state", "county", "enumdist", "reel","page-10",
                                             "page-5"))




plot_data_mover = plot_data




rm(plot_data)
rm(list=ls()[ls()!='df'& !grepl('covars',ls())& !grepl('plot_data', ls())])
gc()


#####

####################################
#### Second Category: Non South ####
####################################

#########################
#### ESTIMATE MODELS ####
#########################


## NO FE
fml_dem = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|0|0|county_state",sep=""))
mod_dem1 = felm(formula=fml_dem, data = df[white==1 & south == 0,] )
sum_dem1 = summary(mod_dem1, robust =T)



## STATE FE
fml_dem_st = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|statefip|0|county_state",sep=""))
mod_dem_st = felm(formula=fml_dem_st, data = df[white==1 & south == 0,] )
sum_dem_st=summary(mod_dem_st, robust =T)


## COUNTY FE
fml_dem_c = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|county_state|0|county_state",sep=""))
mod_dem_c = felm(formula=fml_dem_c, data = df[white==1 & south == 0,] )
sum_dem_c=summary(mod_dem_c, robust =T)


## ENUMDIST FE
fml_dem_ed = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|cnty_enumdist|0|county_state",sep=""))
mod_dem_ed= felm(formula=fml_dem_ed, data = df[white==1 & south == 0,] )
sum_dem_ed=summary(mod_dem_ed, robust =T)



## REEL FE
fml_dem_r = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel|0|county_state",sep=""))
mod_dem_r = felm(formula=fml_dem_r, data = df[white==1 & south == 0,] )
sum_dem_r=summary(mod_dem_r, robust =T)


## REEL PAGE 5 FE
fml_dem_rp5 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page5|0|county_state",sep=""))
mod_dem_rp5= felm(formula=fml_dem_rp5, data = df[white==1 & south == 0,] )
sum_dem_rp5=summary(mod_dem_rp5, robust =T)

## REEL PAGE 10 FE
fml_dem_rp10 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page10|0|county_state",sep=""))
mod_dem_rp10 = felm(formula=fml_dem_rp10, data = df[white==1 & south == 0,] )
sum_dem_rp10 = summary(mod_dem_rp10, robust =T)



## MAKE PLOT DATA
plot_data = as.data.frame(matrix(c(sum_dem1$coefficients['opposite1_dist_1',c(1,2,4)], "controls",  "none"), ncol =5, nrow=1), stringsAsFactors = F)
colnames(plot_data) = c("coef", "se", "p-value", "covars", 
                        "fe")

plot_data = rbind(plot_data, c(sum_dem_st$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "state"))
plot_data = rbind(plot_data, c(sum_dem_c$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "county"))
plot_data = rbind(plot_data, c(sum_dem_r$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "reel"))
plot_data = rbind(plot_data, c(sum_dem_rp5$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-5"))
plot_data = rbind(plot_data, c(sum_dem_rp10$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-10"))
plot_data = rbind(plot_data, c(sum_dem_ed$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "enumdist"))

plot_data$coef=as.numeric(plot_data$coef)
plot_data$se=as.numeric(plot_data$se)
plot_data$`p-value`=as.numeric(plot_data$`p-value`)
plot_data$covars = factor(plot_data$covars, levels = c("no controls", "controls"))
plot_data$fe=factor(plot_data$fe, levels = c("none", "state", "county", "enumdist", "reel","page-10",
                                             "page-5"))




plot_data_south = plot_data


rm(plot_data)
rm(list=ls()[ls()!='df'& !grepl('covars',ls())& !grepl('plot_data', ls())])
gc()


#####

###############################
#### Third Category: Urban ####
###############################

#########################
#### ESTIMATE MODELS ####
#########################


## NO FE
fml_dem = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|0|0|county_state",sep=""))
mod_dem1 = felm(formula=fml_dem, data = df[white==1 & urban == 1,] )
sum_dem1 = summary(mod_dem1, robust =T)


## STATE FE
fml_dem_st = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|statefip|0|county_state",sep=""))
mod_dem_st = felm(formula=fml_dem_st, data = df[white==1 & urban == 1,] )
sum_dem_st=summary(mod_dem_st, robust =T)


## COUNTY FE
fml_dem_c = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|county_state|0|county_state",sep=""))
mod_dem_c = felm(formula=fml_dem_c, data = df[white==1  & urban == 1,] )
sum_dem_c=summary(mod_dem_c, robust =T)


## ENUMDIST FE
fml_dem_ed = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|cnty_enumdist|0|county_state",sep=""))
mod_dem_ed= felm(formula=fml_dem_ed, data = df[white==1 & urban == 1,] )
sum_dem_ed=summary(mod_dem_ed, robust =T)



## REEL FE
fml_dem_r = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel|0|county_state",sep=""))
mod_dem_r = felm(formula=fml_dem_r, data = df[white==1 & urban == 1,] )
sum_dem_r=summary(mod_dem_r, robust =T)




## REEL PAGE 5 FE
fml_dem_rp5 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page5|0|county_state",sep=""))
mod_dem_rp5= felm(formula=fml_dem_rp5, data = df[white==1  & urban == 1,] )
sum_dem_rp5=summary(mod_dem_rp5, robust =T)


## REEL PAGE 10 FE
fml_dem_rp10 = as.formula(paste(paste("democrat ~ ", paste(covars, collapse= "+")),"|reel_page10|0|county_state",sep=""))
mod_dem_rp10 = felm(formula=fml_dem_rp10, data = df[white==1  & urban == 1,] )
sum_dem_rp10 = summary(mod_dem_rp10, robust =T)


## MAKE PLOT DATA
plot_data = as.data.frame(matrix(c(sum_dem1$coefficients['opposite1_dist_1',c(1,2,4)], "controls",  "none"), ncol =5, nrow=1), stringsAsFactors = F)
colnames(plot_data) = c("coef", "se", "p-value", "covars", 
                        "fe")

plot_data = rbind(plot_data, c(sum_dem_st$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "state"))
plot_data = rbind(plot_data, c(sum_dem_c$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "county"))
plot_data = rbind(plot_data, c(sum_dem_r$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "reel"))
plot_data = rbind(plot_data, c(sum_dem_rp5$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-5"))
plot_data = rbind(plot_data, c(sum_dem_rp10$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "page-10"))
plot_data = rbind(plot_data, c(sum_dem_ed$coefficients['opposite1_dist_1', c(1,2,4)], "controls",  "enumdist"))


plot_data$coef=as.numeric(plot_data$coef)
plot_data$se=as.numeric(plot_data$se)
plot_data$`p-value`=as.numeric(plot_data$`p-value`)
plot_data$covars = factor(plot_data$covars, levels = c("no controls", "controls"))
plot_data$fe=factor(plot_data$fe, levels = c("none", "state", "county", "enumdist", "reel","page-10",
                                             "page-5"))




plot_data_urban = plot_data


rm(plot_data)
rm(list=ls()[ls()!='df'& !grepl('covars',ls())& !grepl('plot_data', ls())])
gc()




#####





plot_data_17 = plot_data_mover %>%
  as_tibble %>%
  mutate(analysis = 'No Move in Last 5 Years',
         year  = '2017 Sample') %>%
  bind_rows(plot_data_south %>%
              as_tibble %>%
              mutate(analysis = 'Non-South',
                     year  = '2017 Sample'))%>%
  bind_rows(plot_data_urban %>%
              as_tibble %>%
              mutate(analysis = 'Urban',
                     year  = '2017 Sample'))
rm(plot_data_mover, plot_data_south, plot_data_urban, df)
gc()
############################################################

require(Hmisc)
plot_data = plot_data_0509 %>%
  bind_rows(plot_data_17)%>%
  mutate(fe = capitalize(as.character(fe)),
         fe = ifelse(fe=='Enumdist','District',fe),
         fe = factor(fe, levels = c('None', 'State', 'County', 'Reel', 'District', 'Page-10', 'Page-5')))



## LOAD AESTHETIC FUNCTIONS
source("01-code/r_utils.R")
require(scales)

## MAKE PLOT

p=ggplot(data = plot_data %>% filter(covars=='controls'& !(fe %in% c('Page-10', 'Page-5'))), aes(x = fe, y = coef, color = as.numeric(fe),fill = as.numeric(fe), shape = analysis)) +
  scale_shape_manual(values=c(15:20, 22:25 )) +
  xlab('')+
  ylab('Black Next Door Neighbor Coefficient') + geom_hline(yintercept = 0, alpha = .5, linetype = "dashed")+
  geom_errorbar(aes(ymin=coef-1.96*se, ymax=coef+1.96*se), width=0, size = 1.5, 
                position=position_dodge(.5), alpha=.75) + 
  geom_point(size = 4, position=position_dodge(.5), fill = "white")  +# scale_y_continuous(limits=c(-0.01,0.3),oob=squish)+
  guides(alpha=NULL, fill=NULL)+
  theme_shom()+
  theme(text = element_text(size=18), legend.position="bottom",
                                                   legend.title = element_blank(),
                                                   axis.text.x = element_text(angle = 45, hjust = 1))+
  guides(color='none', fill = 'none')+
  facet_wrap(year~.,nrow = 2, strip.position = 'top')+
  NULL





## SAVE PLOT
ggsave(file = "03-output/01-plots/Fig3.jpeg", plot = p, width = 9, height = 5, units = 'in', dpi=600)




